On Efficiently Acquiring Annotations for Multilingual Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AWT4LPUIU" target="_blank" >RIV/00216208:11320/22:WT4LPUIU - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.acl-short.9" target="_blank" >https://aclanthology.org/2022.acl-short.9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.acl-short.9" target="_blank" >10.18653/v1/2022.acl-short.9</a>
Alternative languages
Result language
angličtina
Original language name
On Efficiently Acquiring Annotations for Multilingual Models
Original language description
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages. In this work, we show that the strategy of joint learning across multiple languages using a single model performs substantially better than the aforementioned alternatives. We also demonstrate that active learning provides additional, complementary benefits. We show that this simple approach enables the model to be data efficient by allowing it to arbitrate its annotation budget to query languages it is less certain on. We illustrate the effectiveness of our proposed method on a diverse set of tasks: a classification task with 4 languages, a sequence tagging task with 4 languages and a dependency parsing task with 5 languages. Our proposed method, whilst simple, substantially outperforms the other viable alternatives for building a model in a multilingual setting under constrained budgets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
ISBN
978-1-955917-22-3
ISSN
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e-ISSN
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Number of pages
17
Pages from-to
69-85
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Dublin, Ireland
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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